Design of optimal digital lattice filter structures based on genetic algorithm

Huang, Chaogeng; Li, Gang; Xu, Zhixing; Yu, Aihua; Chang, Liping

doi:10.1016/j.sigpro.2011.10.011

Cited by 18 publications

(6 citation statements)

References 17 publications

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“…5 In some particular occasions, reliability, stability, redundancy, power, delay or scale of system are considerable in structure design. Although there are an unlimited number of equivalent filter structures realizing a given transfer function, 37 those often used forms are limited, such as frequency sampling and lattice form.…”

Section: Structure Implementation Of Digital Filtersmentioning

confidence: 99%

“…38,39 A new class of lattice-based digital filter structures is derived in Ref. 5. Each stage of the lattice structure can take any one of four elementary lattice structures, which are created by the authors.…”

Section: Structure Implementation Of Digital Filtersmentioning

confidence: 99%

“…For example, a 20-instruction-length chromosome crosses with a 15-instruction-length chromosome with a length range of [5,25] in Fig. 3.…”

Section: Crossover Stagementioning

confidence: 99%

“…Moreover, digital filter synthesis is usually based on some fixed structures or handdesigned ones, such as frequency sampling, lattice and improved lattice. 1,2,5 Those structures maybe fit the target response very well for some special tasks, but the lack of diversity of structures results in less flexibility for a random task. The overall optimization of identification and synthesis for digital filters is still a unresolved problem.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Structure Evolution Based Optimization Algorithm for Low Pass IIR Digital Filter Design

Chen¹,

Liu²,

Yang³

et al. 2017

IJCIS

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Digital filters are generally designed by identifying the transfer functions. Most researches are focused on the goal of approaching the desired frequency response, and take less additional consideration of structure characteristics which can greatly affect the performance of the digital filter. This paper proposes a structure-evolution based optimization algorithm (SEOA) which allows the integrated consideration of structure issues and frequency response specifications in design stage. The method generates digital filter structures by a structurally automatic-generation algorithm (SAGA) which can randomly generate and effectively represent digital structures. The structures, seen as chromosomes, are evolved over genetic algorithm (GA) for the search of the optimal solution in structure space. They are evaluated according to the mean squared error (MSE) between the designed and the desired frequency responses. Simulation results validate that the algorithm designs diversified structures of digital filters and they meet target frequency specifications and structure constraints tightly. It is a promising way for optimized and automated design of digital filters.

show abstract

Section: Structure Implementation Of Digital Filtersmentioning

confidence: 99%

Section: Structure Implementation Of Digital Filtersmentioning

confidence: 99%

“…For example, a 20-instruction-length chromosome crosses with a 15-instruction-length chromosome with a length range of [5,25] in Fig. 3.…”

Section: Crossover Stagementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Structure Evolution Based Optimization Algorithm for Low Pass IIR Digital Filter Design

Chen¹,

Liu²,

Yang³

et al. 2017

IJCIS

View full text Add to dashboard Cite

show abstract

“…Global optimisation methods are tools to search global maximums and minimums. GA [19][20][21] has been used in many different areas to find the global solution. In this paper, we have considered three different evolutionary algorithms.…”

Section: Optimisation Algorithmsmentioning

confidence: 99%

High‐performance Brillouin distributed temperature sensor using Fourier wavelet regularised deconvolution algorithm

Pradhan

Sahu

2014

IET Optoelectronics

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In this study, the authors presented a distributed optical fibre temperature sensor whose performance is improved using the optimisation techniques and Fourier wavelet regularised deconvolution (ForWaRD) method. As the input power launched into the sensing fibre is critical, the authors have considered both conventional optimisation technique and evolutionary optimisation techniques namely: genetic algorithm, differential evolution algorithm and particle swarm optimisation algorithm to increase the stimulated Brillouin scattering (SBS) threshold power. Using the optimised value of the parameters and employing evolutionary computing techniques, the proposed 50 km long temperature sensing system offers a 4.7 dB improvement in SBS threshold power over the design of experiment based system. It is being verified that power of the backscattered signals approximately are the convolution of the input pulsed power and corresponding backscatter optical fibre impulse responses. The Fourier wavelet regularised deconvolution (ForWaRD) method is employed to improve the spatial resolution of the proposed sensing system without reducing the pulse width of the input pulse. Employing ForWaRD technique a 10 m better spatial resolution observed as compared with the Fourier deconvolution technique. The proposed 50 km long temperature sensing system exhibits a temperature resolution of 1.85 K because of suppression of SBS threshold and noise reduction.

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Searching for Dependences within the System of Measuring Stations by Using Symbolic Regression

Gajdoš

Radecký

Vozňák

2013

Nostradamus 2013: Prediction, Modeling and Analysis of Complex Systems

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Design of optimal digital lattice filter structures based on genetic algorithm

Cited by 18 publications

References 17 publications

Structure Evolution Based Optimization Algorithm for Low Pass IIR Digital Filter Design

Structure Evolution Based Optimization Algorithm for Low Pass IIR Digital Filter Design

High‐performance Brillouin distributed temperature sensor using Fourier wavelet regularised deconvolution algorithm

Searching for Dependences within the System of Measuring Stations by Using Symbolic Regression

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